ADDITIONAL MENU
Coffee Type Classification Using Backpropagation Artificial Neural Network
Abstract
Coffee has several types including robusta coffee, arabica coffee and luwak coffee. Each coffee has certain characteristics of color, texture, aroma and also the quality of the taste. Coffee counterfeiting is also common. This coffee counterfeiting usually uses materials such as corn, wheat, soybeans, husks, sticks and robusta coffee beans. So that a model is needed to be able to classify the type of coffee. This research uses artificial neural network machine learning algorithms to identify and classify coffee. Quality training and testing data is needed in this method because it will affect the final results. Initial data is collected via e-nose, with this equipment data on changes in electrical voltage will be obtained from 4 sensors, namely MQ-2, MQ-3, MQ-7 and MQ-135. These 4 features will be used in the classification process. With 900 sets of training data, the test results show that the neural network is able to provide correct classification 99% of the 3 sets of testing data. The results of training and testing show that the neural network formed can identify and distinguish coffee types with good results.
Keywords
Backpropagation; Artificial Neural Network; Classification; Coffee Type; E-Nose
References
Asiah N, Epriyani C, Kurnia A, Ramadhan K, Hidayat SG, Apriyantono A. Profil Kopi Arabika Kintamani Bali. Malang: aepublishing; 2022.
Arwangga AF, Asih IARA, Sudiarta IW. ANALISIS KANDUNGAN KAFEIN PADA KOPI DI DESA SESAOT NARMADA MENGGUNAKAN SPEKTROFOTOMETRI UV-VIS. JURNAL KIMIA. 2016 Jan;10(1):110–4.
Rhardjo P. Kopi. Jakarta: Penebar Swadaya; 2012.
Nugroho MA, Sebatubun MM. KLASIFIKASI VARIETAS KOPI BERDASARKAN GREEN BEAN COFFEE MENGGUNAKAN METODE MACHINE LEARNING. Journal of Information System Management (JOISM). 2020 Jan 31;1(2):1–5.
Rabersyah D, . F, . D. Identifikasi Jenis Bubuk Kopi menggunakan Electronic Nose dengan Metode Pembelajaran Backpropagation. JURNAL NASIONAL TEKNIK ELEKTRO. 2016 Oct 27;5(3):332.
Pahlevi R, Zakaria WA, Kalsum U. ANALISIS KELAYAKAN USAHA AGROINDUSTRI KOPI LUWAK DI KECAMATAN BALIK BUKIT KABUPATEN LAMPUNG BARAT. Jurnal Ilmu Ilmu Agribisnis. 2014 Jan;2(1):48–55.
Winkler-Moser JK, Singh M, Rennick KA, Bakota EL, Jham G, Liu SX, et al. Detection of Corn Adulteration in Brazilian Coffee ( Coffea arabica ) by Tocopherol Profiling and Near-Infrared (NIR) Spectroscopy. J Agric Food Chem. 2015 Dec 16;63(49):10662–8.
Edi Ismanto EPC. JARINGAN SYARAF TIRUAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI KETERSEDIAAN KOMODITI PANGAN PROVINSI RIAU. Rabit : Jurnal Teknologi dan Sistem Informasi Univrab. 2017 Aug 7;2(2):196–209.
Octariadi BC. PENGENALAN POLA TANDA TANGAN MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION. Jurnal Teknoinfo. 2020 Jan 15;14(1):15.
Novita DD, Sesunan AB, Telaumbanua M, Triyono S, Saputra TW. IDENTIFIKASI JENIS KOPI MENGGUNAKAN SENSOR E-NOSE DENGAN METODE PEMBELAJARAN JARINGAN SYARAF TIRUAN BACKPROPAGATION. Jurnal Ilmiah Rekayasa Pertanian dan Biosistem. 2021 Sep 29;9(2):205–17.
Sumanto B, Java DR, Wijaya W, Hendry J. Seleksi Fitur Terhadap Performa Kinerja Sistem E-Nose untuk Klasifikasi Aroma Kopi Gayo. MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer. 2022 Mar 31;21(2):429–38.
Kusairi K, Muthmainnah M, Imam Tazi, Moh. Fajrul Falah. Klasifikasi Pola Aroma Teh Hijau Menggunakan Hidung Elektronik (E-Nose) Berbasis Linear Diskriminan Analisis (LDA). JURNAL PENDIDIKAN MIPA. 2022 Sep 21;12(3):868–74.
Schröer C, Kruse F, Gómez JM. A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Comput Sci. 2021;181:526–34.
Saltz JS. CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. In: 2021 IEEE International Conference on Big Data (Big Data). IEEE; 2021. p. 2337–44.
Suhanda Y, Kurniati I, Norma S. Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik. Jurnal Teknologi Informatika dan Komputer. 2020 Sep 30;6(2):12–20.
Singgalen YA. Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW. Journal of Information System Research (JOSH). 2023 Jul 31;4(4):1343–53.
Dhewayani FN, Amelia D, Alifah DN, Sari BN, Jajuli M. Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM. Jurnal Teknologi dan Informasi. 2022 Mar 31;12(1):64–77.
Pambudi A. PENERAPAN CRISP-DM MENGGUNAKAN MLR K-FOLD PADA DATA SAHAM PT. TELKOM INDONESIA (PERSERO) TBK (TLKM) (STUDI KASUS: BURSA EFEK INDONESIA TAHUN 2015-2022). Jurnal Data Mining dan Sistem Informasi. 2023 Mar 1;4(1):1.
Brownlee J. Data Preparation For Machine Learning. v1.2. 2020.
Masmoudi O, Jaoua M, Jaoua A, Yacout S. Data Preparation in Machine Learning for Condition-based Maintenance. Journal of Computer Science. 2021 Jun 1;17(6):525–38.
Pratama R, Herdiansyah MI, Syamsuar D, Syazili A. Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning. Jurnal Sisfokom (Sistem Informasi dan Komputer). 2023 Mar 17;12(1):96–104.
Christian Y, Qi KOYR. Penerapan K-Means pada Segmentasi Pasar untuk Riset Pemasaran pada Startup Early Stage dengan Menggunakan CRISP-DM. JURIKOM (Jurnal Riset Komputer). 2022 Aug 30;9(4):966.
Hadianto N, Novitasari HB, Rahmawati A. KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK. Jurnal Pilar Nusa Mandiri. 2019 Sep 5;15(2):163–70.
DOI: http://dx.doi.org/10.24014/ijaidm.v7i1.28853
Refbacks
- There are currently no refbacks.
Office and Secretariat:
Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau
Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942
Journal Indexing:
Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti | SINTA | Dimensions | ICI Index Copernicus
IJAIDM Stats